app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'title', 'description', 'article', 'interpretation', 'enable_queue', 'image', 'label',
           'examples', 'intf', 'is_cat', 'classify_image']

# %% app.ipynb 1
from fastai.vision.all import *
import gradio as gr

def is_cat(x):
    return x[0].isupper()

# %% app.ipynb 3
learn = load_learner("model.pkl")

# %% app.ipynb 5
categories = ("Dog", "Cat")

def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

# %% app.ipynb 8
title = "Cat or Dog Classifier"
description = "A Cat or Dog classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
interpretation='default'
enable_queue=True

# %% app.ipynb 9
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ["dog1.jpg", "dog2.jpg", "dog3.jpg", "cat1.jpg", "cat2.jpg"]

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title=title, description=description, article=article, interpretation=interpretation, enable_queue=enable_queue)
intf.launch(inline=False)